[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Discovering Latent Classes for Semi-supervised Semantic Segmentation

  • Conference paper
  • First Online:
Pattern Recognition (DAGM GCPR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12544))

Included in the following conference series:

  • 1289 Accesses

Abstract

High annotation costs are a major bottleneck for the training of semantic segmentation approaches. Therefore, methods working with less annotation effort are of special interest. This paper studies the problem of semi-supervised semantic segmentation, that is only a small subset of the training images is annotated. In order to leverage the information present in the unlabeled images, we propose to learn a second task that is related to semantic segmentation but that is easier to learn and requires less annotated images. For the second task, we learn latent classes that are on one hand easy enough to be learned from the small set of labeled data and are on the other hand as consistent as possible with the semantic classes. While the latent classes are learned on the labeled data, the branch for inferring latent classes provides on the unlabeled data an additional supervision signal for the branch for semantic segmentation. In our experiments, we show that the latent classes boost the accuracy for semi-supervised semantic segmentation and that the proposed method achieves state-of-the-art results on the Pascal VOC 2012 and Cityscapes datasets.

O. Zatsarynna and J. Sawatzky—Contributed equally.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ahn, J., Kwak, S.: Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4981–4990 (2018)

    Google Scholar 

  2. Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What’s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549–565. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_34

    Chapter  Google Scholar 

  3. Briq, R., Moeller, M., Gall, J.: Convolutional simplex projection network for weakly supervised semantic segmentation (2018)

    Google Scholar 

  4. Chaudhry, A., Dokania, P.K., Torr, P.H.: Discovering class-specific pixels for weakly-supervised semantic segmentation. In: British Machine Vision Conference (BMVC) (2017)

    Google Scholar 

  5. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    Article  Google Scholar 

  6. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  7. Dai, D., Sakaridis, C., Hecker, S., Van Gool, L.: Curriculum model adaptation with synthetic and real data for semantic foggy scene understanding. Int. J. Comput. Vis. 128, 1182–1204 (2020)

    Article  Google Scholar 

  8. Everingham, M., Eslami, S.M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. (IJCV) 111(1), 98–136 (2014)

    Article  Google Scholar 

  9. Fan, R., Hou, Q., Cheng, M.-M., Yu, G., Martin, R.R., Hu, S.-M.: Associating inter-image salient instances for weakly supervised semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 371–388. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_23

    Chapter  Google Scholar 

  10. Ge, W., Yang, S., Yu, Y.: Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1277–1286 (2018)

    Google Scholar 

  11. Hong, S., Yeo, D., Kwak, S., Lee, H., Han, B.: Weakly supervised semantic segmentation using web-crawled videos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2224–2232 (2017)

    Google Scholar 

  12. Hou, Q., Massiceti, D., Dokania, P.K., Wei, Y., Cheng, M.-M., Torr, P.H.S.: Bottom-up top-down cues for weakly-supervised semantic segmentation. In: Pelillo, M., Hancock, E. (eds.) EMMCVPR 2017. LNCS, vol. 10746, pp. 263–277. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78199-0_18

    Chapter  Google Scholar 

  13. Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7014–7023 (2018)

    Google Scholar 

  14. Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H.: Adversarial learning for semi-supervised semantic segmentation. In: Proceedings of the British Machine Vision Conference (BMVC) (2018)

    Google Scholar 

  15. Jin, B., Segovia, M.V.O., Ssstrunk, S.: Webly supervised semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1705–1714 (2017)

    Google Scholar 

  16. Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: weakly supervised instance and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1665–1674 (2017)

    Google Scholar 

  17. Kolesnikov, A., Lampert, C.H.: Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 695–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_42

    Chapter  Google Scholar 

  18. Kurmi, V.K., Bajaj, V., Venkatesh, K.S., Namboodiri, V.P.: Curriculum based dropout discriminator for domain adaptation. In: British Machine Vision Conference (BMVC) (2019)

    Google Scholar 

  19. Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: Ficklenet: weakly and semi-supervised semantic image segmentation using stochastic inference. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  20. Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: Frame-to-frame aggregation of active regions in web videos for weakly supervised semantic segmentation. In: IEEE International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  21. Li, H., He, X., Barnes, N., Wang, M.: Learning hough transform with latent structures for joint object detection and pose estimation. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 116–129. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-27674-8_11

    Chapter  Google Scholar 

  22. Li, K., Wu, Z., Peng, K., Ernst, J., Fu, Y.: Guided attention inference network. IEEE Trans. Pattern Anal. Mach. Intell. 42, 2996–3010 (2019)

    Article  Google Scholar 

  23. Li, Q., Arnab, A., Torr, P.H.: Weakly- and semi-supervised panoptic segmentation. In: European Conference on Computer Vision (ECCV), pp. 106–124 (2018)

    Google Scholar 

  24. Lian, Q., Lv, F., Duan, L., Gong, B.: Constructing self-motivated pyramid curriculums for cross-domain semantic segmentation: a non-adversarial approach. In: IEEE International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  25. Lin, D., Dai, J., Jia, J., He, K., Sun, J.: Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3159–3167 (2016)

    Google Scholar 

  26. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  27. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440 (2015)

    Google Scholar 

  28. Mittal, S., Tatarchenko, M., Brox, T.: Semi-supervised semantic segmentation with high- and low-level consistency. IEEE Tran. Pattern Anal. Mach. Intell. (2019)

    Google Scholar 

  29. Nguyen, A., Kanoulas, D., Caldwell, D.G., Tsagarakis, N.: Object-based affordances detection with convolutional neural networks and dense conditional random fields. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017)

    Google Scholar 

  30. Oh, S.J., Benenson, R., Khoreva, A., Akata, Z., Fritz, M., Schiele, B.: Exploiting saliency for object segmentation from image level labels. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5038–5047 (2017)

    Google Scholar 

  31. Papandreou, G., Chen, L.C., Murphy, K.P., Yuille, A.L.: Weakly- and semi-supervised learning of a deep convolutional network for semantic image segmentation. In: International Conference on Computer Vision (ICCV), pp. 1742–1750 (2015)

    Google Scholar 

  32. Pathak, D., Krähenbühl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: International Conference on Computer Vision (ICCV), pp. 1796–1804 (2015)

    Google Scholar 

  33. Pinheiro, P.H.O., Collobert, R.: From image-level to pixel-level labeling with convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1713–1721 (2015)

    Google Scholar 

  34. Qi, X., Liu, Z., Shi, J., Zhao, H., Jia, J.: Augmented feedback in semantic segmentation under image level supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 90–105. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_6

    Chapter  Google Scholar 

  35. Razavi, N., Gall, J., Kohli, P., van Gool, L.: Latent hough transform for object detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 312–325. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33712-3_23

    Chapter  Google Scholar 

  36. Richard, A., Kuehne, H., Gall, J.: Weakly supervised action learning with RNN based fine-to-coarse modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1273–1282 (2017)

    Google Scholar 

  37. Roy, A., Todorovic, S.: Combining bottom-up, top-down, and smoothness cues for weakly supervised image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7282–7291 (2017)

    Google Scholar 

  38. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  39. Sakaridis, C., Dai, D., Van Gool, L.: Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation. In: IEEE International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  40. Shimoda, W., Yanai, K.: Distinct class-specific saliency maps for weakly supervised semantic segmentation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 218–234. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_14

    Chapter  Google Scholar 

  41. Song, C., Huang, Y., Ouyang, W., Wang, L.: Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  42. Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., Schroers, C.: Normalized cut loss for weakly-supervised CNN segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1818–1827 (2018)

    Google Scholar 

  43. Tang, M., Perazzi, F., Djelouah, A., Ayed, I.B., Schroers, C., Boykov, Y.: On regularized losses for weakly-supervised CNN segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 524–540. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_31

    Chapter  Google Scholar 

  44. Wang, X., You, S., Li, X., Ma, H.: Weakly-supervised semantic segmentation by iteratively mining common object features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1354–1362 (2018)

    Google Scholar 

  45. Wei, Y., Feng, J., Liang, X., Cheng, M.M., Zhao, Y., Yan, S.: Object region mining with adversarial erasing: a simple classification to semantic segmentation approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6488–6496 (2017)

    Google Scholar 

  46. Wei, Y., et al.: STC: a simple to complex framework for weakly-supervised semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2314–2320 (2017)

    Article  Google Scholar 

  47. Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., Huang, T.S.: Revisiting dilated convolution: a simple approach for weakly- and semi-supervised semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7268–7277 (2018)

    Google Scholar 

  48. Zhang, Y., David, P., Gong, B.: Curriculum domain adaptation for semantic segmentation of urban scenes. In: IEEE International Conference on Computer Vision (ICCV), pp. 2039–2049 (2017)

    Google Scholar 

  49. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929 (2016)

    Google Scholar 

  50. Zhu, X., Anguelov, D., Ramanan, D.: Capturing long-tail distributions of object subcategories, pp. 915–922 (2014)

    Google Scholar 

Download references

Acknowledgement

This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) GA 1927/5-1 and under Germany’s Excellence Strategy EXC 2070 – 390732324.

Author information

Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Olga Zatsarynna, Johann Sawatzky & Juergen Gall

  2. EyewareTech, Martigny, Switzerland

    Johann Sawatzky

Authors
  1. Olga Zatsarynna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Johann Sawatzky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juergen Gall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olga Zatsarynna .

Editor information

Editors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Zeynep Akata

  2. University of Tübingen, Tübingen, Germany

    Andreas Geiger

  3. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 22326 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zatsarynna, O., Sawatzky, J., Gall, J. (2021). Discovering Latent Classes for Semi-supervised Semantic Segmentation. In: Akata, Z., Geiger, A., Sattler, T. (eds) Pattern Recognition. DAGM GCPR 2020. Lecture Notes in Computer Science(), vol 12544. Springer, Cham. https://doi.org/10.1007/978-3-030-71278-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71278-5_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71277-8

  • Online ISBN: 978-3-030-71278-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics